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Detection and correction of spectral and spatial misregistrations for hyperspectral data using phase correlation

Naoto Yokoya1, Norihide Miyamura, Akira Iwasaki

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Hyperspectral imaging misregistrations distort object signatures. This study presents a method using phase correlation and cubic spline interpolation to accurately detect and correct these spectral and spatial errors for improved sensor calibration.

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Area of Science:

  • Remote Sensing
  • Optical Engineering
  • Image Processing

Background:

  • Hyperspectral imaging sensors are prone to spectral and spatial misregistrations caused by optical aberrations.
  • These misregistrations distort object-specific spectral signatures, leading to reduced classification accuracy.
  • Accurate calibration is crucial for reliable hyperspectral data analysis.

Purpose of the Study:

  • To develop and validate a method for detecting and correcting spectral and spatial misregistrations in hyperspectral images.
  • To assess the efficacy of the proposed method for onboard calibration of hyperspectral sensors.

Main Methods:

  • Utilized phase correlation for accurate detection of spectral and spatial misregistrations.
  • Employed cubic spline interpolation with estimated properties to correct distorted spectral signatures.
  • Applied the method to the Hyperion visible near-infrared subsystem as a case study.

Main Results:

  • Phase correlation effectively detected spectral and spatial misregistrations.
  • Cubic spline interpolation successfully modified spectral signatures.
  • Postlaunch estimation accuracy of sensor characteristics was comparable to prelaunch measurements.

Conclusions:

  • The proposed method accurately detects and corrects hyperspectral image misregistrations.
  • This approach enables reliable onboard calibration of hyperspectral sensors.
  • Improved spectral signature fidelity enhances hyperspectral data analysis and classification accuracy.